Image data of a measurement object to be examined can be obtained with the aid of modern medical diagnostic methods such as, for example, X-ray computed tomography (CT). As a rule, the measurement object examined is a patient.
X-ray computed tomography—noted below by CT for short—is a specific X-ray recording method that differs fundamentally in image structure from the classical X-ray tomography method. What is obtained in the case of CT photographs are transverse sectional images, that is to say images of body slices that are aligned substantially perpendicular to the body axis. The tissue-specific physical quantity represented in the image is the distribution of the attenuation value of X-ray radiation μ(x, y) in the sectional plane. The CT image is obtained by reconstructing the one-dimensional projections, supplied by the measurement system used, of the two-dimensional distribution of μ(x, y) from numerous different angles of view.
CT images can be generated both by way of a CT unit with a scanning system that can circulate endlessly, and by way of a C-arc unit with a scanning system that can rotate only by less than 360°. The abbreviation “CT”, for example in “CT raw data”, is used below with reference to both types of unit.
The projection data are determined from the intensity I of an X-ray after its path through the slice to be imaged, and by its original intensity I0 at the X-ray source in accordance with the absorption law                               1          ⁢                                           ⁢          n          ⁢                                    I              0                        I                          =                              ∫            L                    ⁢                                    μ              ⁡                              (                                  x                  ,                  y                                )                                      ⁢                          ⅆ              l                                                          (        1        )            
The integration path L represents the track of the X-ray considered through the two-dimensional attenuation distribution μ(x, y). An image projection is then compiled from the measured values, obtained with the aid of the X-rays from one direction of view, of the line integrals though the object slice.
The projections originating from the most varied directions—characterized by the projection angle α—are obtained by a combined X-ray tube/detector system that rotates in the slice plane about the object. The units currently most employed are so-called “fan beam units” in the case of which tubes and an array of detectors (a linear arrangement of detectors) rotate together in the slice plane about a center of rotation, which is also the center of the circular measuring field. The “parallel beam units”, which are affected by very long measuring times, will not be explained here. However, it may be pointed out that it is possible to convert from fan to parallel projections and vice versa. Thus, an embodiment of the present invention, which is to be explained with the aid of a fan beam unit, can also be applied without restriction to parallel beam units.
In fan beam geometry, a CT photograph includes line integral measured values −In(I/I0) of incident beams that are characterized by a two-dimensional coupling of the projection angle α∈[0, 2π) and the fan angles β∈[−β0, β0] defining the detector positions (β0 being half the fan opening angle). Since the measuring system has only a finite number k of detector elements, and a measurement consists of a finite number y of projections, this coupling is discrete and can be represented by a matrix:{tilde over (p)}(αy, βk):[0.2π]×[−β0, β0]  (2)or{tilde over (p)}(y, k):(1, 2 . . . NP)×(1, 2 . . . NS)  (3)The matrix {tilde over (p)}(y, k) is called a sinogram for fan beam geometry. The number of projections y and the number of channels k are of the order of magnitude of 1000.
Taking the logarithms in accordance with the equation (1) thus yields the line integrals of all the projections                                           p            ⁡                          (                              α                ;                β                            )                                =                                    1              ⁢              n              ⁢                                                I                  0                                I                                      =                                          ∫                L                            ⁢                                                μ                  ⁡                                      (                                          x                      ,                      y                                        )                                                  ⁢                                  ⅆ                  l                                                                    ,                            (                  3          ⁢          a                )            the totality of which is also denoted as radon transform of the distribution μ(x, y). Such a radon transformation is reversible. It is thereby being possible to calculate μ(x, y) from p(α, β) by back transformation (inverse radon transformation).
It is customary in this back transformation to apply a convolution algorithm in which the line integrals per projection are firstly convolved with the aid of a special function and then backprojected onto the image plane along the original beam directions. This special function, by which the convolution algorithm is essentially characterized, is denoted as “convolution core”. Owing to the mathematical configuration of the convolution core, the possibility exists of specifically influencing the image quality in the reconstruction of a CT image from the CT raw data.
For example, an appropriate convolution core can be used to emphasize high frequencies in order to increase the spatial resolution in the image. Alternatively, a convolution core of an appropriately different type can be used to damp high frequencies in order to reduce the image noise. Thus, it may be stated in summary that when reconstructing images in computed tomography the image characteristic, which is characterized by image sharpness/image noise and image contrast (two behaving in a complementary fashion relative to one another), can be influenced by selecting a suitable convolution core. There is a direct proportionality between image sharpness and image noise in this case, that is to say when the image sharpness is increased the noise is increased to the same extent.
No further investigation is now to be made into the principle of image reconstruction in CT by calculating the μ value distribution. A detailed description of CT image reconstruction is to be found, for example, in “Bildgebende Systeme für die medizinische Diagnostik” [“Imaging systems for medical diagnostics”], 3rd Edition, Munich: Publicis MCD Verlag, 1995, Publisher: Morneburg Heinz, ISBN 3-89578-002-2, the entire contents of which are hereby incorporated herein by reference.
However, the task of image reconstruction is not yet concluded with the calculation of the μ value distribution of the transirradiated slice. The distribution of the attenuation coefficient μ represents in the field of medical application only an anatomical structure that still needs to be represented in the form of an X-ray image.
In line with a proposal from G. N. Hounsfield, it has become generally customary to convert the values of the linear attenuation coefficient μ (which has the unit of measurement cm−1) to a dimensionless scale in which water takes the value 0 and air the value −1000. The conversion formula to this “CT number” is:                               CT-number                =                                            μ              -                              μ                water                                                    μ              water                                ⁢          1000                                    (        4        )            
The unit of the CT number is called the “Hounsfield unit” (HU). This scale, denoted as the “Hounsfield scale”, is very well suited to representing anatomical tissue, since the unit HU expresses the deviation in parts per thousand from μwater, and the μ values of most bodily substances differ only slightly from the μ value of water. Only whole numbers are used from the number range (from −1000 for air up to approximately 3000) as carriers of image information.
However, the representation of the entire scale range of approximately 4000 values would far exceed the discrimination threshold of the human eye. In addition, the viewer is frequently interested only in a small section of the attenuation value range, for example the differentiation of gray and white brain substance, which differ only by about 10 HU.
Use is made of so-called image windowing for this reason. Only part of the CT value scale is selected in this case, and is spread over all available gray levels. Even small attenuation differences within the chosen window thus become perceptible gray tone differences, and all the CT values below the window are represented black and all the CT values above the window white. The image window can be varied arbitrarily both in terms of its central level and in terms of its width.
It is of interest in computed tomography to undertake multiplanar reformattings (MPR or secondary sections). Multiplanar reformattings are arbitrarily inclined, flat CT images calculated from a volumetric data record (also denoted as primary data record and usually represented by thin axial layers). Since the pixels generally do not occupy the position defined in the volumetric data record, and the layer thickness of an MPR is intended to be able to be set arbitrarily, it is necessary in this case to interpolate suitably. Particularly in the case of more recent CT units, the resolution of a volumetric data record is virtually isotropic. For this reason, it is possible to calculate from such a volumetric data record high-quality MPRs whose quality does not differ from that of the primary images.
However, it is likewise of interest during the course of a good diagnostic image evaluation to manipulate the image characteristic of an MPR—essentially characterized by sharpness and noise—by use of suitable filters. Sharpness and noise of the reformattings are substantially determined by sharpness and noise of the primary, axial images as well as by the layer thickness set when generating the MPR.
The representation of CT images, in particular those of MPRs, with a different image characteristic is therefore of interest because a different evaluation (that is to say a different clinical assessment) of the same photograph of the corresponding tissue requires a different sort of representation of the recorded tissue.
In the prior art, a targeted manipulation of the image characteristic of secondary sections is achieved by determining from the raw data a new volumetric data record by way of a new image reconstruction with the aid of changed convolution core parameters, and using this new primary data record to subsequently regenerate the original secondary sections. This refers to a reconstruction with the aid of a different convolution core that has a different characteristic, for example precisely a different sharpness.